1. Field of the Invention
The field of this invention relates to a method and apparatus configuring at least one frequency dependent (FD), in-phase/quadrature (I/Q), imbalance compensation filter within, say, a radio frequency (RF) module.
2. Background of the Invention
A primary focus and application of the present invention is the field of radio frequency (RF) receivers capable of use in wireless telecommunication applications. FIG. 1 illustrates a simplified block diagram of an example of an RF receiver architecture 100 such as may be used in a wireless telecommunication apparatus; for example such as user equipment in third generation partnership project (3GPP™) parlance. In the illustrated example, the RF receiver architecture 100 comprises an in-phase (I) branch and a quadrature (Q) branch. An input of each of the ‘I’ and ‘Q’ branches is operably coupled to an antenna 105. In each of the ‘I’ and ‘Q’ branches, RF signals received via the antenna 105 are provided to a mixer component 110, 115, which mixes the a received RF signal with a sine wave signal from a local oscillator 120 to down convert a wanted frequency signal of the received RF signal to an intermediate or baseband frequency. The sine wave signal from the local oscillator 120 provided to one of the ‘I’ or ‘Q’ branches (the ‘Q’ branch in the illustrated example) is phase shifted 90 degrees. The down converted signals output by the respective mixers 110, 115 in each of the ‘I’ and ‘Q’ branches are then provided to low pass filters (hI(n), hQ(n)) 130, 135 which filer out unwanted frequency components of the down converted signals. The filtered signals are then provided to analogue to digital converters (ADCs) 140, 145, which output digital signals (zI(n), zQ(n)) 150, 155 representative of the filtered, down converted signals.
In practical receivers, the analogue components within the in-phase and quadrature branches, and in particular the respective low pass filters 130, 135, tend not to be exactly matched, and thus can degrade the image rejection ratio (IRR) of the receiver, thereby resulting in performance loss. The use of high-order modulation schemes in modern wireless standards, such as for example 64-QAM modulation used in the LTE (Long Term Evolution) wireless standards and 256-QAM modulation used in IEEE 802.11ac wireless standards, dictates high IRR requirements of 40 to 50 dB. Furthermore, the use of large bandwidths such as, for example, 20 MHz in LTE and 160 MHz in IEEE 802.11ac, result in significant frequency dependent I/Q imbalances.
FIG. 2 illustrates a simplified block diagram of an example of a typical I/Q imbalance compensation architecture such as may be implemented for the RF receiver 100 of FIG. 1. Trying to reduce the I/Q imbalance using analogue design would significantly increase the cost of the RF chipset. As such, compensating for the I/Q imbalance is preferably implemented within the digital domain, for example within a digital signal processing component 200, but costs, area and/or power must be kept low. The I/Q imbalance typically comprises two components: a frequency dependent (FD) component and a frequency independent (FI) components. In the illustrated example, the I/Q imbalance compensation architecture comprises an FD I/Q imbalance compensation filter (β(n)) 210 and an FI I/Q imbalance compensation scalar component (α) 220 and adder component 230. The FD I/Q imbalance compensation filter (β(n)) 210 is implemented within the Q-branch of the digital domain in the illustrated example and arranged to filter the digital Q-branch signal 155 such that the joint filtering performed by the Q-branch analogue low pass filter 135 and the digital FD I/Q imbalance compensation filter (β(n)) 210 matches the filtering performed by the I-branch analogue low pass filter 130. The FI I/Q imbalance compensation scalar component 220 and the adder component 230 compensate for FI I/Q imbalance.
The FD I/Q imbalance compensation filter (β(n)) 210 may alternatively implemented within the I-branch of the digital domain and arranged to filter the digital I-branch signal 150 such that the joint filtering performed by the I-branch analogue low pass filter 130 and the digital FD I/Q imbalance compensation filter (β(n)) 210 matches the filtering performed by the Q-branch analogue low pass filter 135.
In the example illustrated in FIG. 2, the incoming digital signal before compensation may be expressed as:z(n)=zI(n)+jzQ(n)  [Equation 1]
Following the FD I/Q imbalance compensation, the signal may be expressed as:u(n)=uI(n)+juQ(n)  [Equation 2]
Notably, uI(n)=zI(n). Whilst the basic architecture illustrated in FIG. 2 for compensating for I/Q imbalance is commonly implemented within RF receivers, various techniques for configuring the FD I/Q imbalance compensation filter (β(n)) 210 have been proposed.
One approach for configuring the FD I/Q imbalance compensation filter (β(n)) 210 involves training based techniques. One such training based technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 is illustrated in FIG. 3a, and comprises using specially designed training sequences through loopback from the transmitter to the receiver of the RF transceiver module, through the RF front-end. Such a technique is deterministic (non-stochastic), and therefor fast. However, such a technique requires the transmitter frequency to be offset from the receiver frequency to specific values for configuration, which may require difficult modifications to the transceiver architecture.
A further training based technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 is illustrated in FIG. 3b, and comprises the use of an envelope detector whereby in a first step (with switch s1 open and switch s2 closed) the transmitter of the RF transceiver is configured using the envelope detector, and in a second step (with switch s1 closed and switch s2 open) the receiver of the RF transceiver is configured using the envelope detector and a loopback from the transmitter. Once again, such a technique is deterministic (non-stochastic), and therefor fast. However, this technique is sensitive to transmitter distortion, such as linearity and harmonics.
In addition to the disadvantages already mentioned, such training based approaches also require modifications in the baseband or RF architectures, and require complex additional circuitry such as envelope detectors or tone-generators. Furthermore, such training based approaches do not facilitate ‘on-the-fly’ configuration.
Another approach for configuring the FD I/Q imbalance compensation filter (β(n)) 210 involves the use of blind algorithms. For example, one known blind algorithm technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 is illustrated in FIG. 3c, and comprises iterative estimation of β(n) using non-linear equations, such as:E{uI(n)uI(n−k)}=E{uQ(n)uQ(n−k)}∀k  [Equation 1]where uQ(n)=β(n)*zQ(n) and uI(n)=zI(n). Autocorrelations of zI(n) and zQ(n) at various delays are computed by averaging several incoming data samples. A benefit of such a technique is that β(n) may be solved using a circularity property. However, such a solution using a non-linear iterative technique involves using a matrix inversion, and thus the complexity of such a solution is prohibitive for filter lengths of greater than three.
A second known blind algorithm technique for configuring the FD I/Q imbalance compensation filter β(n) 210 is illustrated in FIG. 3d, and comprises adaptively updating the estimate for the k-th tap of the FD I/Q imbalance compensation filter (β(n)) 210 using the equation:βk(n)=βk(n−1)+μ(uI(n)uI(n−k)−uQ(n)uQ(n−k))∀k  [Equation 2]This approach is effectively a crude approximation of the previous blind algorithm technique, which has the advantage of reduced complexity and memory requirements. However this method results in an inferior configuration of the FD I/Q imbalance compensation filter (β(n)) 210, and thus an inferior performance of the receiver.
A further known blind algorithm technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 comprises obtaining the autocorrelations of zI(n) and zQ(n) as for the first blind algorithm technique illustrated in FIG. 3c and described above. Discrete Fourier Transforms (DFT's) of the autocorrelations are then computed and divided to obtain a magnitude of β(n). Assuming a minimum phase β(n), the phase of n) is then computed using the Hilbert transform. The Inverse Discrete Fourier Transform (IDFT) of the frequency response is then computed to obtain the impulse response of β(n). Advantageously, this technique produces an almost linear solution of lower complexity than the first blind algorithm technique illustrated in FIG. 3c and described above. However, it is still a relatively high complexity solution requiring significant processing/memory resources. Furthermore, the underlying spectral estimation scheme is non-parametric and hence performance is in general poor. In addition, the performance degrades in a dispersive channel for on-the-fly configuration, and performance is sensitive to spectral nulls resulting in less flexibility in relation to range of sampling rates.
In addition to the disadvantages already mentioned, such known blind algorithm based approaches in general suffer from high-complexity, for example requiring non-linear equations to be solved using iterative methods.
An alternative known technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 is illustrated in FIG. 3e, and comprises setting the phase difference between the ‘I’ and ‘Q’ branches to be 45°. Prior to the introduction of any I/Q imbalance, the ‘I’ and ‘Q’ signals are orthogonal to each other. By shifting the phase difference between the ‘I’ and ‘Q’ branches to 45°, the I-branch signal leaks to the Q-branch through the FD I/Q imbalance compensation filter (β(n)) 210. By using the I-branch signal as a reference, β(n) may be estimated using adaptive noise cancellation. Advantageously, the computational complexity of such a technique is similar to a least mean square (LMS) computation, and thus is significantly lower than for the known blind algorithm techniques. However, setting the phase difference between the ‘I’ and ‘Q’ branches to be 45° requires the local oscillator (LO) frequency to be quadrupled, and requires a gain loss of 3 dB, typically (LO's with 25% duty-cycle), to be compensated for. As such, this technique results in an increased power consumption of the RF circuitry, and also results in a degradation of the noise performance when limited by headroom. Such increases in power consumption and degradation of the noise performance are particularly problematic for higher frequency designs such as used for LTE and IEEE 802.11ac wireless standards.
Accordingly, there is a need for a technique for configuring the FD I/Q imbalance compensation filter (β(n)) 210 that requires no, or minimal, additional circuitry or modifications to the transceiver front-end architecture, which involve increases in power consumption and/or cost, and that comprises a reduced complexity compared with known blind algorithm techniques to minimise required digital logic.